© 2014. Ajay Pal Singh Chauhan & Amar Partap Singh. This is a research/review paper, distributed under the terms of the Creative
Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-
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Global Journal of Computer Science and Technology: G
Interdisciplinary
Volume 14 Issue 4 Version 1.0 Year 2014
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 0975-4172 & Print ISSN: 0975-4350
Virtual Grader for Apple Quality Assessment using Fruit Size
and Illumiation Features
By Ajay Pal Singh Chauhan & Amar Partap Singh
Sant Longowal Institute of Engineering & Technology, India
Abstract- The present paper reports on the development of an intelligent virtual grader for assessing apple
quality using machine vision. The heart of the proposed virtual grader was executed in the form of K-Nearest
Neighbor (K-NN) classifier designed on the architecture of Euclidean distance metric. K-NN classifier is
executed for this particular application due to its robustness to the noisy environment. The present study
revealed that fruit surface illumination is one of the major deterministic parameters affecting accuracy
substantially while assessing apple quality based on fruit size. The performance of the proposed virtual grader
was examined experimentally under different conditions of fruit surface illumination. An industrial grade camera
connected to an image grabber was used to implement the proposed industrial-grade virtual grader using
machine vision. Results of this study are quite promising with an achievement of 99% efficiency at 100%
repeatability when fruit surface is exposed to an optimal value of 310 lux. However, such an attempt has not
been made earlier.
Keywords: classifier, machine vision, intensity, perimeter, hydraulic radius.
GJCST-G Classification: D.3.3
VirtualGraderforAppleQualityAssessmentusingFruitSizeandIllumiationFeatures
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